Kun Yang
29 Papers
2 Citations
Kun Yang is an academic researcher. The author has contributed to research in topics: Environmental science & Precipitation. The author has an hindex of 4, co-authored 28 publications.
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Papers
Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China
Tianjie Zhao,Haishen Lü,Jiancheng Shi,Michael H. Cosh,Dabin Ji,Lingmei Jiang,Qian Cui,Huiguo Lu,Kun Yang,Jean-Pierre Wigneron,Xiaojun Li,Yonghua Zhu,Lu Hu,Zhiqing Peng,Ye-long Zeng,Xiaoyi Wang,Chuen Siang Kang +16 more
TL;DR: In this article , triple collocation analysis (TCA) was applied to all possible triplets to verify the reliability and robustness of the results, including local acquisition time, physical surface temperature, and vegetation optical depth (VOD).
113
TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations
Yaozhi Jiang,Kun Yang,Youcun Qi,Xu Zhou,Jie He,Huiguo Lu,Xin Li,Ying Ruan Chen,Xiaodong Li,Bingrong Zhou,Ali Mamtimin,Changkun Shao,Xiao ling Ma,Jiaxin Tian,Jianhong Zhou +14 more
TL;DR: In this article , Yang et al. presented a long-term (1979-2020) high-resolution (1/30∘, daily) precipitation dataset (TPHiPr) for the Third Pole (TP) region by merging the atmospheric simulation-based ERA5_CNN with gauge observations from more than 9000 rain gauges, using the climatologically aided interpolation and random forest methods.
81
Projection of China’s future runoff based on the CMIP6 mid-high warming scenarios
TL;DR: In this paper , the Equidistant Cumulative Distribution Function method (EDCDFm) was used to perform downscaling and bias correction in daily precipitation, daily maximum temperature, and daily minimum temperature for six CMIP6 models based on the historical gridded data from the high-resolution China Meteorological Forcing Dataset (CMFD).
27
Convolutional neural network-based homogenization for constructing a long-term global surface solar radiation dataset
TL;DR: In this paper , a convolutional neural network-based homogenization method is developed to use a reanalysis dataset (ERA5) as a bridge to homogenize a 36-year (1983-2018) satellite-based solar radiation dataset (so-called ISCCP-ITP) with 10-km spatial resolution and 3-hr interval.
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